E.07 The role of the neurologist in advanced multiple sclerosis: the patient’s perspective
Bibliographic record
Abstract
Background: Few evidence-based disease-modifying treatments exist for progressive multiple sclerosis (MS). How can neurologists best care for patients with advanced MS? Little is known about how patients with progressive MS view their relationship with their treating neurologist, and if the role of the neurologist matches their needs and preferences. Methods: A qualitative cross-sectional analysis of patient preferences regarding the role of the neurologist in their care. Patients with progressive MS and an EDSS score of 6 or more were invited to participate. Patients and caregivers completed separate written questionnaires and were then interviewed by one of the authors. Data were subjected to thematic coding to group common themes and the distribution of themes among different disability sub-groups was analyzed. Results: Full results will be available at the time of the conference. Preliminary results suggest that the neurologist has an important role in updating patients on the progress of their disease and responding to questions. Patients are fearful of becoming dependent on others for their care. The concept of palliative care is unfamiliar to most patients. Conclusions: Despite a lack of disease-modifying treatments for progressive multiple sclerosis, patients believe that the neurologist has an important role in their care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".